What Is Bucket in Data Structure?


Larry Thompson

A bucket is an essential component in data structures, particularly in hash tables or hash maps. It serves as a container to store elements or data items that have the same hash value. In other words, a bucket is a collection of key-value pairs where the keys are hashed to determine the specific bucket they belong to.

Understanding the Purpose of Buckets

In data structures like hash tables, buckets are used to handle collisions. Collisions occur when two or more keys are hashed to the same index or location in the hash table. To address this issue, each index in the hash table has a corresponding bucket that holds multiple elements.

The main purpose of buckets is to provide an efficient way of organizing and retrieving data. By using buckets, we can avoid storing all elements directly at their respective indices. Instead, we store them in separate buckets associated with those indices.

Bucket Structure

A bucket can be implemented using various data structures like arrays, linked lists, or even trees depending on the specific requirements and constraints of the application.

Array-Based Buckets

In array-based buckets, each index in the hash table points to an array element that serves as a bucket. This array can hold multiple key-value pairs associated with that particular index. When a collision occurs, new elements are appended to this array.

  • Advantages: Array-based buckets offer constant time access when retrieving elements from within the same bucket.
  • Disadvantages: As more collisions happen and new elements are added to an array-based bucket, its size may increase significantly, resulting in performance degradation due to frequent resizing operations.

Linked List-Based Buckets

In linked list-based buckets, each index in the hash table points to the head of a linked list. Each node in the linked list contains a key-value pair. When a collision occurs, new nodes are appended to the linked list associated with that index.

  • Advantages: Linked list-based buckets can dynamically allocate memory for new elements without worrying about resizing issues. They also offer constant time access within the same bucket.
  • Disadvantages: Traversing a linked list-based bucket to find an element can take linear time, especially if the bucket contains a large number of elements.

Key Operations on Buckets

Buckets support several important operations:

  • Insertion: When adding a new element, it is hashed to determine its corresponding bucket and then inserted into that bucket based on the selected data structure (e.g., as an array element or linked list node).
  • Deletion: To remove an element, it is first hashed to locate its bucket and then removed from that specific bucket.
  • Search: To find an element, its key is hashed to identify its corresponding bucket. Then, within that bucket, we search for the desired key-value pair using appropriate techniques such as linear search (in array-based buckets) or traversing (in linked list-based buckets).

In Summary

Buckets play a crucial role in handling collisions and organizing data efficiently in hash tables or hash maps. They provide a way to store multiple elements with similar hash values. By using different data structures like arrays or linked lists as buckets, we can ensure efficient access and manipulation of data within these structures.

Understanding the concept of buckets is essential for anyone working with hash tables or other data structures that rely on hashing. By employing buckets effectively, we can optimize performance and ensure faster retrieval of data, even in the presence of collisions.

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